Similarity matching of a competitor&#39;s products

ABSTRACT

In embodiments of the present invention improved capabilities are described for identifying a classification scheme associated with product attributes of a grouping of products of an entity, receiving a record of data relating to an item of a competitor to the entity, the classification of which is uncertain, receiving a dictionary of attributes associated with products, and assigning a product code to the item, based on probabilistic matching among the attributes in the classification scheme, the attributes in the dictionary of attributes and at least one known attribute of the item.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of the following U.S. provisionalapplications: App. No. 60/887,573 filed on Jan. 31, 2007 and entitled“Analytic Platform,” App. No. 60/891,508 filed on Feb. 24, 2007 andentitled “Analytic Platform,” App. No. 60/891,936 filed on Feb. 27, 2007and entitled “Analytic Platform,” App. No. 60/952,898 filed on Jul. 31,2007 and entitled “Analytic Platform.”

This application is a continuation-in-part of U.S. application Ser. No.12/021,263 filed on Jan. 28, 2008 and entitled “Associating a GrantingMatrix with an Analytic Platform”, which claims the benefit of thefollowing U.S. provisional applications: App. No. 60/886,798 filed onJan. 26, 2007 and entitled “A Method of Aggregating Data,” App. No.60/886,801 filed on Jan. 26, 2007 and entitled “Utilizing AggregatedData.”

Each of the above applications is incorporated by reference herein inits entirety.

BACKGROUND

1. Field

This invention relates to methods and systems for analyzing data, andmore particularly to methods and systems for aggregating, projecting,and releasing data.

2. Description of Related Art

Currently, there exists a large variety of data sources, such as censusdata or movement data received from point-of-sale terminals, sample datareceived from manual surveys, panel data obtained from the inputs ofconsumers who are members of panels, fact data relating to products,sales, and many other facts associated with the sales and marketingefforts of an enterprise, and dimension data relating to dimensionsalong which an enterprise wishes to understand data, such as in order toanalyze consumer behaviors, to predict likely outcomes of decisionsrelating to an enterprise's activities, and to project from sample setsof data to a larger universe. Conventional methods of synthesizing,aggregating, and exploring such a universe of data comprise techniquessuch as OLAP, which fix aggregation points along the dimensions of theuniverse in order to reduce the size and complexity of unifiedinformation sets such as OLAP stars. Exploration of the unifiedinformation sets can involve run-time queries and query-timeprojections, both of which are constrained in current methods by apriori decisions that must be made to project and aggregate the universeof data. In practice, going back and changing the a priori decisions canlift these constraints, but this requires an arduous and computationallycomplex restructuring and reprocessing of data.

According to current business practices, unified information sets andresults drawn from such information sets can be released to thirdparties according to so-called “releasability” rules. Theses rules mightapply to any and all of the data from which the unified information setsare drawn, the dimensions (or points or ranges along the dimensions),the third party (or members or sub-organizations of the third party),and so on. Given this, there can be a complex interaction between thedata, the dimensions, the third party, the releasability rules, thelevels along the dimensions at which aggregations are performed, theinformation that is drawn from the unified information sets, and so on.In practice, configuring a system to apply the releasability rules is anerror-prone process that requires extensive manual set up and results ina brittle mechanism that cannot adapt to on-the-fly changes in data,dimensions, third parties, rules, aggregations, projections, userqueries, and so on.

Various projection methodologies are known in the art. Still otherprojection methodologies are subjects of the present invention. In anycase, different projection methodologies provide outputs that havedifferent statistical qualities. Analysts are interested in specifyingthe statistical qualities of the outputs at query-time. In practice,however, the universe of data and the projection methodologies that areapplied to it are what drive the statistical qualities. Existing methodsallow an analyst to choose a projection methodology and thereby affectthe statistical qualities of the output, but this does not satisfy theanalyst's desire to directly dictate the statistical qualities.

Information systems are a significant bottle neck for market analysisactivities. The architecture of information systems is often notdesigned to provide on-demand flexible access, integration at a verygranular level, or many other critical capabilities necessary to supportgrowth. Thus, information systems are counter-productive to growth.Hundreds of market and consumer databases make it very difficult tomanage or integrate data. For example, there may be a separate databasefor each data source, hierarchy, and other data characteristics relevantto market analysis. Different market views and product hierarchiesproliferate among manufacturers and retailers. Restatements of datahierarchies waste precious time and are very expensive. Navigation fromamong views of data, such as from global views to regional toneighborhood to store views is virtually impossible, because there aredifferent hierarchies used to store data from global to region toneighborhood to store-level data. Analyses and insights often take weeksor months, or they are never produced. Insights are often sub-optimalbecause of silo-driven, narrowly defined, ad hoc analysis projects.Reflecting the ad hoc nature of these analytic projects are the analytictools and infrastructure developed to support them. Currently, marketanalysis, business intelligence, and the like often use rigid data cubesthat may include hundreds of databases that are impossible to integrate.These systems may include hundreds of views, hierarchies, clusters, andso forth, each of which is associated with its own rigid data cube. Thismay make it almost impossible to navigate from global uses that areused, for example, to develop overall company strategy, down to specificprogram implementation or customer-driven uses. These ad hoc analytictools and infrastructure are fragmented and disconnected.

In sum, there are many problems associated with the data used for marketanalysis, and there is a need for a flexible, extendable analyticplatform, the architecture for which is designed to support a broadarray of evolving market analysis needs. Furthermore, there is a needfor better business intelligence in order to accelerate revenue growth,make business intelligence more customer-driven, to gain insights aboutmarkets in a more timely fashion, and a need for data projection andrelease methods and systems that provide improved dimensionalflexibility, reduced query-time computational complexity, automaticselection and blending of projection methodologies, and flexibly appliedreleasability rules.

SUMMARY

In embodiments, systems and methods may involve using a platform asdisclosed herein for applications described herein where the systems andmethods involve identifying a classification scheme associated withproduct attributes of a grouping of products of an entity. It may alsoinvolve receiving a record of data relating to an item of a competitorto the entity, the classification of which is uncertain. It may alsoinvolve receiving a dictionary of attributes associated with products.It may also involve assigning a product code to the item, based onprobabilistic matching among the attributes in the classificationscheme, the attributes in the dictionary of attributes and at least oneknown attribute of the item.

In embodiments, the product attribute may be a nutritional level, abrand, a product category, a physical attribute, may be based on one ormore part of a stock keeping unit (SKU) or some other type of productattribute. For example, a product attribute may be ‘Reebok’. In thiscase, the product attribute is a brand. The product attributes may beprovided by a similarity facility. The similarity facility may associateeach attribute with a weight for probabilistic matching.

In embodiments, the product attribute may be the physical attribute. Thephysical attribute may be a flavor, a scent, packaging type, a productlaunch type, display location or some other type of physical attribute.For example, the packaging type may be ‘hard bound’ or ‘paperback’ incase of a book.

These and other systems, methods, objects, features, and advantages ofthe present invention will be apparent to those skilled in the art fromthe following detailed description of the preferred embodiment and thedrawings. Capitalized terms used herein (such as relating to titles ofdata objects, tables, or the like) should be understood to encompassother similar content or features performing similar functions, exceptwhere the context specifically limits such terms to the use herein.

BRIEF DESCRIPTION OF THE FIGURES

The invention and the following detailed description of certainembodiments thereof may be understood by reference to the followingfigures:

FIG. 1 illustrates an analytic platform for performing data analysis.

FIG. 2 depicts a similarities matching process.

FIG. 3 depicts a logical flow of a similarity matching process using acompetitor's products.

DETAILED DESCRIPTION

Referring to FIG. 1, the methods and systems disclosed herein arerelated to improved methods for handling and using data and metadata forthe benefit of an enterprise. An analytic platform 100 may support andinclude such improved methods and systems. The analytic platform 100 mayinclude, in certain embodiments, a range of hardware systems, softwaremodules, data storage facilities, application programming interfaces,human-readable interfaces, and methodologies, as well as a range ofapplications, solutions, products, and methods that use various outputsof the analytic platform 100, as more particularly detailed herein,other embodiments of which would be understood by one of ordinary skillin the art and are encompassed herein. Among other components, theanalytic platform 100 includes methods and systems for providing variousrepresentations of data and metadata, methodologies for acting on dataand metadata, an analytic engine, and a data management facility that iscapable of handling disaggregated data and performing aggregation,calculations, functions, and real-time or quasi-real-time projections.In certain embodiments, the methods and systems enable much more rapidand flexible manipulation of data sets, so that certain calculations andprojections can be done in a fraction of the time as compared with oldergeneration systems.

In embodiments, data compression and aggregations of data, such as factdata sources 102, and dimension data sources 104, may be performed inconjunction with a user query such that the aggregation dataset can bespecifically generated in a form most applicable for generatingcalculations and projections based on the query. In embodiments, datacompression and aggregations of data may be done prior to, inanticipation of, and/or following a query. In embodiments, an analyticplatform 100 (described in more detail below) may calculate projectionsand other solutions dynamically and create hierarchical data structureswith custom dimensions that facilitate the analysis. Such methods andsystems may be used to process point-of-sale (POS) data, retailinformation, geography information, causal information, surveyinformation, census data and other forms of data and forms ofassessments of past performance (e.g. estimating the past sales of acertain product within a certain geographical region over a certainperiod of time) or projections of future results (e.g. estimating thefuture or expected sales of a certain product within a certaingeographical region over a certain period of time). In turn, variousestimates and projections can be used for various purposes of anenterprise, such as relating to purchasing, supply chain management,handling of inventory, pricing decisions, the planning of promotions,marketing plans, financial reporting, and many others.

Referring still to FIG. 1 an analytic platform 100 is illustrated thatmay be used to analyze and process data in a disaggregated or aggregatedformat, including, without limitation, dimension data defining thedimensions along which various items are measured and factual data aboutthe facts that are measured with respect to the dimensions. Factual datamay come from a wide variety of sources and be of a wide range of types,such as traditional periodic point-of-sale (POS) data, causal data (suchas data about activities of an enterprise, such as in-store promotions,that are posited to cause changes in factual data), household paneldata, frequent shopper program information, daily, weekly, or real timePOS data, store database data, store list files, stubs, dictionary data,product lists, as well as custom and traditional audit data. Furtherextensions into transaction level data, RFID data and data fromnon-retail industries may also be processed according to the methods andsystems described herein.

In embodiments, a data loading facility 108 may be used to extract datafrom available data sources and load them to or within the analyticplatform 100 for further storage, manipulation, structuring, fusion,analysis, retrieval, querying and other uses. The data loading facility108 may have the a plurality of responsibilities that may includeeliminating data for non-releasable items, providing correct venue groupflags for a venue group, feeding a core information matrix with relevantinformation (such as and without limitation statistical metrics), or thelike. In an embodiment, the data loading facility 108 eliminatenon-related items. Available data sources may include a plurality offact data sources 102 and a plurality of dimension data sources 104.Fact data sources 102 may include, for example, facts about salesvolume, dollar sales, distribution, price, POS data, loyalty cardtransaction files, sales audit files, retailer sales data, and manyother fact data sources 102 containing facts about the sales of theenterprise, as well as causal facts, such as facts about activities ofthe enterprise, in-store promotion audits, electronic pricing and/orpromotion files, feature ad coding files, or others that tend toinfluence or cause changes in sales or other events, such as facts aboutin-store promotions, advertising, incentive programs, and the like.Other fact data sources may include custom shelf audit files, shipmentdata files, media data files, explanatory data (e.g., data regardingweather), attitudinal data, or usage data. Dimension data sources 104may include information relating to any dimensions along which anenterprise wishes to collect data, such as dimensions relating toproducts sold (e.g. attribute data relating to the types of productsthat are sold, such as data about UPC codes, product hierarchies,categories, brands, sub-brands, SKUs and the like), venue data (e.g.store, chain, region, country, etc.), time data (e.g. day, week,quad-week, quarter, 12-week, etc.), geographic data (includingbreakdowns of stores by city, state, region, country or other geographicgroupings), consumer or customer data (e.g. household, individual,demographics, household groupings, etc.), and other dimension datasources 104. While embodiments disclosed herein relate primarily to thecollection of sales and marketing-related facts and the handling ofdimensions related to the sales and marketing activities of anenterprise, it should be understood that the methods and systemsdisclosed herein may be applied to facts of other types and to thehandling of dimensions of other types, such as facts and dimensionsrelated to manufacturing activities, financial activities, informationtechnology activities, media activities, supply chain managementactivities, accounting activities, political activities, contractingactivities, and many others.

In an embodiment, the analytic platform 100 comprises a combination ofdata, technologies, methods, and delivery mechanisms brought together byan analytic engine. The analytic platform 100 may provide a novelapproach to managing and integrating market and enterprise informationand enabling predictive analytics. The analytic platform 100 mayleverage approaches to representing and storing the base data so that itmay be consumed and delivered in real-time, with flexibility and openintegration. This representation of the data, when combined with theanalytic methods and techniques, and a delivery infrastructure, mayminimize the processing time and cost and maximize the performance andvalue for the end user. This technique may be applied to problems wherethere may be a need to access integrated views across multiple datasources, where there may be a large multi-dimensional data repositoryagainst which there may be a need to rapidly and accurately handledynamic dimensionality requests, with appropriate aggregations andprojections, where there may be highly personalized and flexiblereal-time reporting 190, analysis 192 and forecasting capabilitiesrequired, where there may be a need to tie seamlessly and on-the-flywith other enterprise applications 184 via web services 194 such as toreceive a request with specific dimensionality, apply appropriatecalculation methods, perform and deliver an outcome (e.g. dataset,coefficient, etc.), and the like.

The analytic platform 100 may provide innovative solutions toapplication partners, including on-demand pricing insights, emergingcategory insights, product launch management, loyalty insights, dailydata out-of-stock insights, assortment planning, on-demand audit groups,neighborhood insights, shopper insights, health and wellness insights,consumer tracking and targeting, and the like.

A decision framework may enable new revenue and competitive advantagesto application partners by brand building, product innovation,consumer-centric retail execution, consumer and shopper relationshipmanagement, and the like. Predictive planning and optimizationsolutions, automated analytics and insight solutions, and on-demandbusiness performance reporting may be drawn from a plurality of sources,such as InfoScan, total C-scan, daily data, panel data, retailer directdata, SAP, consumer segmentation, consumer demographics, FSP/loyaltydata, data provided directly for customers, or the like.

The analytic platform 100 may have advantages over more traditionalfederation/consolidation approaches, requiring fewer updates in asmaller portion of the process. The analytic platform 100 may supportgreater insight to users, and provide users with more innovativeapplications. The analytic platform 100 may provide a unified reportingand solutions framework, providing on-demand and scheduled reports in auser dashboard with summary views and graphical dial indicators, as wellas flexible formatting options. Benefits and products of the analyticplatform 100 may include non-additive measures for custom productgroupings, elimination of restatements to save significant time andeffort, cross-category visibility to spot emerging trends, provide atotal market picture for faster competitor analysis, provide granulardata on demand to view detailed retail performance, provide attributedriven analysis for market insights, and the like.

The analytic capabilities of the present invention may provide foron-demand projection, on-demand aggregation, multi-source master datamanagement, and the like. On-demand projection may be derived directlyfor all possible geographies, store and demographic attributes, pergeography or category, with built-in dynamic releasability controls, andthe like. On-demand aggregation may provide both additive andnon-additive measures, provide custom groups, provide cross-category orgeography analytics, and the like. Multi-source master data managementmay provide management of dimension member catalogue and hierarchyattributes, processing of raw fact data that may reduce harmonizationwork to attribute matching, product and store attributes storedrelationally, with data that may be extended independently of fact data,and used to create additional dimensions, and the like.

In addition, the analytic platform 100 may provide flexibility, whilemaintaining a structured user approach. Flexibility may be realized withmultiple hierarchies applied to the same database, the ability to createnew custom hierarchies and views, rapid addition of new measures anddimensions, and the like. The user may be provided a structured approachthrough publishing and subscribing reports to a broader user base, byenabling multiple user classes with different privileges, providingsecurity access, and the like. The user may also be provided withincreased performance and ease of use, through leading-edge hardware andsoftware, and web application for integrated analysis.

In embodiments, the data available within a fact data source 102 and adimension data source 104 may be linked, such as through the use of akey. For example, key-based fusion of fact 102 and dimension data 104may occur by using a key, such as using the Abilitec Key softwareproduct offered by Acxiom, in order to fuse multiple sources of data.For example, such a key can be used to relate loyalty card data (e.g.,Grocery Store 1 loyalty card, Grocery Store 2 loyalty card, andConvenience Store 1 loyalty card) that are available for a singlecustomer, so that the fact data from multiple sources can be used as afused data source for analysis on desirable dimensions. For example, ananalyst might wish to view time-series trends in the dollar salesallotted by the customer to each store within a given product category.

In embodiments the data loading facility may comprise any of a widerange of data loading facilities, including or using suitableconnectors, bridges, adaptors, extraction engines, transformationengines, loading engines, data filtering facilities, data cleansingfacilities, data integration facilities, or the like, of the type knownto those of ordinary skill in the art. In various embodiments, there aremany situations where a store will provide POS data and causalinformation relating to its store. For example, the POS data may beautomatically transmitted to the facts database after the salesinformation has been collected at the stores POS terminals. The samestore may also provide information about how it promoted certainproducts, its store or the like. This data may be stored in anotherdatabase; however, this causal information may provide one with insighton recent sales activities so it may be used in later sales assessmentsor forecasts. Similarly, a manufacturer may load product attribute datainto yet another database and this data may also be accessible for salesassessment or projection analysis. For example, when making suchanalysis one may be interested in knowing what categories of productssold well or what brand sold well. In this case, the causal storeinformation may be aggregated with the POS data and dimension datacorresponding to the products referred to in the POS data. With thisaggregation of information one can make an analysis on any of therelated data.

Referring still to FIG. 1, data that is obtained by the data loadingfacility 108 may be transferred to a plurality of facilities within theanalytic platform 100, including the data mart 114. In embodiments thedata loading facility 108 may contain one or more interfaces 182 bywhich the data loaded by the data loading facility 108 may interact withor be used by other facilities within the platform 100 or external tothe platform. Interfaces to the data loading facility 108 may includehuman-readable user interfaces, application programming interfaces(APIs), registries or similar facilities suitable for providinginterfaces to services in a services oriented architecture, connectors,bridges, adaptors, bindings, protocols, message brokers, extractionfacilities, transformation facilities, loading facilities and other dataintegration facilities suitable for allowing various other entities tointeract with the data loading facility 108. The interfaces 182 maysupport interactions with the data loading facility 108 by applications184, solutions 188, reporting facilities 190, analyses facilities 192,services 194 or other entities, external to or internal to anenterprise. In embodiments these interfaces are associated withinterfaces 182 to the platform 100, but in other embodiments directinterfaces may exist to the data loading facility 108, either by othercomponents of the platform 100, or by external entities.

Referring still to FIG. 1, in embodiments the data mart facility 114 maybe used to store data loaded from the data loading facility 108 and tomake the data loaded from the data loading facility 108 available tovarious other entities in or external to the platform 100 in aconvenient format. Within the data mart 114 facilities may be present tofurther store, manipulate, structure, subset, merge, join, fuse, orperform a wide range of data structuring and manipulation activities.The data mart facility 114 may also allow storage, manipulation andretrieval of metadata, and perform activities on metadata similar tothose disclosed with respect to data. Thus, the data mart facility 114may allow storage of data and metadata about facts (including salesfacts, causal facts, and the like) and dimension data, as well as otherrelevant data and metadata. In embodiments, the data mart facility 114may compress the data and/or create summaries in order to facilitatefaster processing by other of the applications 184 within the platform100 (e.g. the analytic server 134). In embodiments the data martfacility 114 may include various methods, components, modules, systems,sub-systems, features or facilities associated with data and metadata.

In certain embodiments the data mart facility 114 may contain one ormore interfaces 182 (not shown on FIG. 1), by which the data loaded bythe data mart facility 114 may interact with or be used by otherfacilities within the platform 100 or external to the platform.Interfaces to the data mart facility 114 may include human-readable userinterfaces, application programming interfaces (APIs), registries orsimilar facilities suitable for providing interfaces to services in aservices oriented architecture, connectors, bridges, adaptors, bindings,protocols, message brokers, extraction facilities, transformationfacilities, loading facilities and other data integration facilitiessuitable for allowing various other entities to interact with the datamart facility 114. These interfaces may comprise interfaces 182 to theplatform 100 as a whole, or may be interfaces associated directly withthe data mart facility 114 itself, such as for access from othercomponents of the platform 100 or for access by external entitiesdirectly to the data mart facility 1 14. The interfaces 182 may supportinteractions with the data mart facility 114 by applications 184,solutions 188, reporting facilities 190, analyses facilities 192,services 194 (each of which is describe in greater detail herein) orother entities, external to or internal to an enterprise.

In certain optional embodiments, the security facility 118 may be anyhardware or software implementation, process, procedure, or protocolthat may be used to block, limit, filter or alter access to the datamart facility 114, and/or any of the facilities within the data martfacility 114, by a human operator, a group of operators, anorganization, software program, bot, virus, or some other entity orprogram. The security facility 118 may include a firewall, an anti-virusfacility, a facility for managing permission to store, manipulate and/orretrieve data or metadata, a conditional access facility, a loggingfacility, a tracking facility, a reporting facility, an asset managementfacility, an intrusion-detection facility, an intrusion-preventionfacility or other suitable security facility.

Still referring to FIG. 1, the analytic platform 100 may include ananalytic engine 134. The analytic engine 134 may be used to build anddeploy analytic applications or solutions or undertake analytic methodsbased upon the use of a plurality of data sources and data types. Amongother things, the analytic engine 134 may perform a wide range ofcalculations and data manipulation steps necessary to apply models, suchas mathematical and economic models, to sets of data, including factdata, dimension data, and metadata. The analytic engine 134 may beassociated with an interface 182, such as any of the interfacesdescribed herein.

The analytic engine 134 may interact with a model storage facility 148,which may be any facility for generating models used in the analysis ofsets of data, such as economic models, econometric models, forecastingmodels, decision support models, estimation models, projection models,and many others. In embodiments output from the analytic engine 134 maybe used to condition or refine models in the model storage 148; thus,there may be a feedback loop between the two, where calculations in theanalytic engine 134 are used to refine models managed by the modelstorage facility 148.

In embodiments, a security facility 138 of the analytic engine 134 maybe the same or similar to the security facility 118 associated with thedata mart facility 114, as described herein. Alternatively, the securityfacility 138 associated with the analytic engine 134 may have featuresand rules that are specifically designed to operate within the analyticengine 134.

As illustrated in FIG. 1, the analytic platform 100 may contain a masterdata management hub 150 (MDMH). In embodiments the MDMH 150 may serve asa central facility for handling dimension data used within the analyticplatform 100, such as data about products, stores, venues, geographies,time periods and the like, as well as various other dimensions relatingto or associated with the data and metadata types in the data sources102, 104, the data loading facility 108, the data mart facility 114, theanalytic engine 134, the model storage facility 148 or variousapplications, 184, solutions 188, reporting facilities 190, analyticfacilities 192 or services 194 that interact with the analytic platform100. The MDMH 150 may in embodiments include a security facility 152, aninterface 158, a data loader 160, a data manipulation and structuringfacility 162, and one or more staging tables 164. The data loader 160may be used to receive data. Data may enter the MDMH from varioussources, such as from the data mart 114 after the data mart 114completes its intended processing of the information and data that itreceived as described herein. Data may also enter the MDMH 150 through auser interface 158, such as an API or a human user interface, webbrowser or some other interface, of any of the types disclosed herein orin the documents incorporated by reference herein. The user interface158 may be deployed on a client device, such as a PDA, personalcomputer, laptop computer, cellular phone, or some other client devicecapable of handling data. In embodiments, the staging tables 164 may beincluded in the MDMH 150.

In embodiments, a matching facility 180 may be associated with the MDMH150. The matching facility 180 may receive an input data hierarchywithin the MDMH 150 and analyze the characteristics of the hierarchy andselect a set of attributes that are salient to a particular analyticinterest (e.g., product selection by a type of consumer, product salesby a type of venue, and so forth). The matching facility 180 may selectprimary attributes, match attributes, associate attributes, blockattributes and prioritize the attributes. The matching facility 180 mayassociate each attribute with a weight and define a set of probabilisticweights. The probabilistic weights may be the probability of a match ora non-match, or thresholds of a match or non-match that is associatedwith an analytic purpose (e.g., product purchase). The probabilisticweights may then be used in an algorithm that is run within aprobabilistic matching engine (e.g., IBM QualityStage). The output ofthe matching engine may provide information on, for example, otherproducts which are appropriate to include in a data hierarchy, theuntapped market (i.e. other venues) in which a product isprobabilistically more likely to sell well, and so forth. Inembodiments, the matching facility 180 may be used to generateprojections of what types of products, people, customers, retailers,stores, store departments, etc. are similar in nature and therefore theymay be appropriate to combine in a projection or an assessment.

As illustrated in FIG. 1, the analytic platform 100 may include aprojection facility 178. A projection facility 178 may be used toproduce projections, whereby a partial data set (such as data from asubset of stores of a chain) is projected to a universe (such as all ofthe stores in a chain), by applying appropriate weights to the data inthe partial data set. A wide range of potential projection methodologiesexist, including cell-based methodologies, store matrix methodologies,iterative proportional fitting methodologies, virtual censusmethodologies, and others. The methodologies can be used to generateprojection factors. As to any given projection, there is typically atradeoff among various statistical quality measurements associated withthat type of projection. Some projections are more accurate than others,while some are more consistent, have less spillage, are more closelycalibrated, or have other attributes that make them relatively more orless desirable depending on how the output of the projection is likelyto be used. In embodiments of the platform 100, the projection facility178 takes dimension information from the MDMH 150 or from another sourceand provides a set of projection weightings along the applicabledimensions, typically reflected in a matrix of projection weights, whichcan be applied at the data mart facility 114 to a partial data set inorder to render a projected data set. The projection facility 178 mayhave an interface 182 of any of the types disclosed herein.

As shown in FIG. 1, an interface 182 may be included in the analyticplatform 100. In embodiments, data may be transferred to the MDMH 150 ofthe platform 100 using a user interface 182. The interface 182 may be aweb browser operating over the Internet or within an intranet or othernetwork, it may be an analytic engine 134, an application plug-in, orsome other user interface that is capable of handling data. Theinterface 182 may be human readable or may consist of one or moreapplication programming interfaces, or it may include variousconnectors, adaptors, bridges, services, transformation facilities,extraction facilities, loading facilities, bindings, couplings, or otherdata integration facilities, including any such facilities describedherein or in documents incorporated by reference herein.

As illustrated in FIG. 1, the platform 100 may interact with a varietyof applications 184, solutions 188, reporting facilities 190, analyticfacilities 192 and services 194, such as web services, or with otherplatforms or systems of an enterprise or external to an enterprise. Anysuch applications 184, solutions 188, reporting facilities 190, analyticfacilities 192 and services 194 may interact with the platform 100 in avariety of ways, such as providing input to the platform 100 (such asdata, metadata, dimension information, models, projections, or thelike), taking output from the platform 100 (such as data, metadata,projection information, information about similarities, analytic output,output from calculations, or the like), modifying the platform 100(including in a feedback or iterative loop), being modified by theplatform 100 (again optionally in a feedback or iterative loop), or thelike.

In embodiments one or more applications 184 or solutions 188 mayinteract with the platform 100 via an interface 182. Applications 184and solutions 188 may include applications and solutions (consisting ofa combination of hardware, software and methods, among other components)that relate to planning the sales and marketing activities of anenterprise, decision support applications, financial reportingapplications, applications relating to strategic planning, enterprisedashboard applications, supply chain management applications, inventorymanagement and ordering applications, manufacturing applications,customer relationship management applications, information technologyapplications, applications relating to purchasing, applications relatingto pricing, promotion, positioning, placement and products, and a widerange of other applications and solutions.

In embodiments, applications 184 and solutions 188 may include analyticoutput that is organized around a topic area. For example, theorganizing principle of an application 184 or a solution 188 may be anew product introduction. Manufacturers may release thousands of newproducts each year. It may be useful for an analytic platform 100 to beable to group analysis around the topic area, such as new products, andorganize a bundle of analyses and workflows that are presented as anapplication 184 or solution 188. Applications 184 and solutions 188 mayincorporate planning information, forecasting information, “what if?”scenario capability, and other analytic features. Applications 184 andsolutions 188 may be associated with web services 194 that enable userswithin a client's organization to access and work with the applications184 and solutions 188.

In embodiments, the analytic platform 100 may facilitate deliveringinformation to external applications 184. This may include providingdata or analytic results to certain classes of applications 184. Forexample and without limitation, an application may include enterpriseresource planning/backbone applications 184 such as SAP, including thoseapplications 184 focused on Marketing, Sales & Operations Planning andSupply Chain Management. In another example, an application may includebusiness intelligence applications 184, including those applications 184that may apply data mining techniques. In another example, anapplication may include customer relationship management applications184, including customer sales force applications 184. In anotherexample, an application may include specialty applications 184 such as aprice or SKU optimization application. The analytic platform 100 mayfacilitate supply chain efficiency applications 184. For example andwithout limitation, an application may include supply chain models basedon sales out (POS/FSP) rather than sales in (Shipments). In anotherexample, an application may include RFID based supply chain management.In another example, an application may include a retailer co-op toenable partnership with a distributor who may manage collective stockand distribution services. The analytic platform 100 may be applied toindustries characterized by large multi-dimensional data structures.This may include industries such as telecommunications, elections andpolling, and the like. The analytic platform 100 may be applied toopportunities to vend large amounts of data through a portal with thepossibility to deliver highly customized views for individual users witheffectively controlled user accessibility rights. This may includecollaborative groups such as insurance brokers, real estate agents, andthe like. The analytic platform 100 may be applied to applications 184requiring self monitoring of critical coefficients and parameters. Suchapplications 184 may rely on constant updating of statistical models,such as financial models, with real-time flows of data and ongoingre-calibration and optimization. The analytic platform 100 may beapplied to applications 184 that require breaking apart and recombininggeographies and territories at will.

In embodiments, a matching facility 180 may be a similarity facility 180may be associated with the MDMH 150. The similarity facility 180 mayreceive an input data hierarchy within the MDMH 150 and analyze thecharacteristics of the hierarchy and select a set of attributes that aresalient to a particular analytic interest (e.g., product selection by atype of consumer, product sales by a type of venue, and so forth). Thesimilarity facility 180 may select primary attributes, match attributes,associate attributes, block attributes and prioritize the attributes.The similarity facility 180 may associate each attribute with a weightand define a set of probabilistic weights. The probabilistic weights maybe the probability of a match or a non-match, or thresholds of a matchor non-match that is associated with an analytic purpose (e.g., productpurchase). The probabilistic weights may then be used in an algorithmthat is run within a probabilistic matching engine (e.g., IBMQualityStage). The output of the matching engine may provide informationon, for example, other products which are appropriate to include in adata hierarchy, the untapped market (i.e. other venues) in which aproduct is probabilistically more likely to sell well, and so forth. Inembodiments, the similarity facility 180 may be used to generateprojections of what types of products, people, customers, retailers,stores, store departments, etc. are similar in nature and therefore theymay be appropriate to combine in a projection or an assessment.

In embodiments, the MDMH 150 may accommodate a blend of disaggregatedand pre-aggregated data as necessitated by a client's needs. Forexample, a client in the retail industry may have a need for a rolling,real-time assessment of store performance within a sales region. Theability of the MDMH 150 to accommodate twinkle data, and the like maygive the client useful insights into disaggregated sales data as itbecomes available and make it possible to create projections based uponit and other available data. At the same time, the client may havepre-aggregated data available for use, for example a competitor's salesdata, economic indicators, inventory, or some other dataset. The MDMH150 may handle the dimension data needed to combine the use of thesediverse data sets.

Referring to FIG. 1 and FIG. 2, several types of data managementchallenges may be grouped into a class of problems known as similarity;which may be described as the problem of finding the commonalities orsimilarities within data structures. While there may be situations wheredata comparisons, fusions, combinations, aggregations and the like canbe made directly (e.g. through an explicit reference to the same itemname) there are many situations where there exist two things that arecharacterized by various attributes but where the attributes do notmatch. In such situations, in certain embodiments, a similaritiesfacility 180 may be used to determine if the characteristics (e.g. theidentified attributes in a fact or dimensions database) of the twothings are close enough for the things to be called “similar.” Inembodiments, the similarities facility 180 may use a probabilistic basedmethodology for the determination of similarity. The probabilisticmethodology may use input (e.g. user input or API input) to determine ifthe two things are similar within a certain degree of confidence and/orthe probabilistic methodology may produce an indication of how likelythe two things are to be similar. There are many processes that can beperformed on the data once two or more things are determined to besimilar. For example, data associated with the two things may beaggregated or fused together such that the information pertaining to thetwo things can be used as a projection of one whole. In certainembodiments, the similarity information may be used to generate newattributes or other descriptions to be associated with the thing beinganalyzed. For example, once a certain product is identified as similarto a certain class of products, data indicating such may be associatedwith the certain product. New attribute data may be associated with anitem and the information may be loaded into a dimensions database suchthat data associated with the item, and the item itself, can be used inprojections or assessments.

A similarities facility 180 according to the principles of the presentinvention may be used to assess the similarity of products, items,departments, stores, environments, real estate, competitors, markets,regions, performance, regional performance, and a variety of otherthings. For example, in the management of retail market information, thesimilarity problem may manifest itself as a need to identify similarstores for purposes of projecting regional performance from a sample of,as the need to identify the competing or comparison set of products forany given item; as the need to identify similar households for purposesof bias correction; as the need to properly place new or previouslyunclassified or reclassified items in a product hierarchy; or for otherprojections and assessments. In another example, again from the retailindustry, automated item placement may pose a problem. Often the currentsolution is labor intensive and may take from eight to twelve weeks fora new product to get properly placed within a store, department, shelfor otherwise. This delay inhibits the effectiveness of the analysis ofnew product introductions and in the tactical monitoring of categoryareas for a daily data service. However, these application sets need theproduct list to be up to date. In addition, the management of customretail hierarchies may require that items from all other retailers inthe market be placed inside that hierarchy, and often the structure ofthat hierarchy is not based on existing attributes. This may mean thatthe logic of the hierarchy itself must be discovered and then all otheritems from the market must be ‘similarly’ organized. In the currentenvironment, this process takes months and is prone to error. Inembodiments of the present invention, issues of similarity are automatedusing techniques such as rules-based matching, algorithmic matching, andother similarities methods. In the present example, the similaritiesfacility 180 may be used to assess the similarity of the new productwith existing products to determine placement information. Once thesimilar matches are made, new attributes may be added to the new productdescription such that it is properly grouped physically in the store andelectronically within the data structures to facilitate properassessments and projections.

The similarity of entities may be associated with the concept ofgrouping entities for the purpose of reporting. One purpose may be tocodify the rules placing entities into a specific classificationdefinition. Some examples of such specific classification definitionsmay be item similarity for use in automatic classification andhierarchy, venue similarity for use in projections and imputations,consumer similarity in areas like ethnicity and economics used fordeveloping weighting factors, or the like.

There may be certain matching requirements used by the similaritiesfacility 180 in the determination of similarity. For example, scenariosfor matching similarity may involve determining similar items related toa single specified item, where the similarities engine is programmed toidentify all the items in the repository that are similar to it withrespect to some specific criteria (e.g. one or more attributes listed inthe dimensions database). The similarities engine may also or instead beprogrammed to analyze a list of items, where all the items in therepository are similar to each of the items in the list with respect tosome specific criteria. Likewise, the similarities facility 180 mayanalyze an item within a list of items, where group items are placedinto classifications of similar items with respect to some specificcriteria; or the like.

In embodiments, the similarities facility 180 may use a probabilisticmatching engine where the probabilistic matching engine compares all orsome subset of attributes to determine the similarity. Each of theattributes may be equally considered in the probabilistic evaluation orcertain attributes may be weighted with greater relevance. Thesimilarities facility 180 may select certain attributes to weigh withgreater relevance and/or it may receive input to determine whichattributes to weight. For example, the similarities engine mayautomatically select and disproportionately weight the attributesassociated with ‘scent’ when assessing the products that it understandsto be deodorants. In other embodiments, a user may determine whichattributes or fields to disproportionally weight. For example, a usermay determine a priority for the weighting of certain attributes (e.g.attributes within a list of attributes identified in a dimensionsdatabase), and load the prioritization, weighting or other suchinformation into the similarities facility 180. The similaritiesfacility 180 may then run a probabilistic matching engine with theweights as a factor in determining the extent to which things aresimilar.

An advantage of using probabilistic matching for doing similarity may berelated to an unduplication process. The matching for unduplication andsimilarity may be similar. However, they may be based on different setsof attributes. For unduplication, all attributes may be used since thesystem may be looking for an exact match or duplicate item. This maywork by matching a list against itself. In similarity, the system may belooking for items that are similar with regard to physical attributes,not the same exact item.

For two entities to be similar, they may have to be evaluated by aspecific similarity measure. In most cases, they may have to be in thesame domain, but this also may depend on the similarity measure that isused. The similarity measure that the system may use is theprobabilistic matching of physical attributes of items, such as adeodorant keycat (or deodorant “key category”), where a keycat is ablock of items that have a similar set of attributes. In this case,since the item may be a domain, and venue is a domain, an item may notbe looked at as being similar to a venue.

The concept of similarity may be based on the similarity of the valuesof attributes that describe specific entities. This may involvedeveloping similarity measures and the metadata to support them. Theprocess may include deciding the purpose of the similarity; selectingthe set of entities to be used in the similarity process; analyzing thecharacteristics (attributes and values) of each entity in the set ofpossible similar entitles; deciding which attributes will be used aspart of the similarity measure; deciding on the priority and weight ofthe set of attributes and their values; defining the similarity measure;defining all the probabilistic weights needed to perform the similaritymeasure; defining the thresholds, such as automatic definite match,automatic definite non-match, or undecided match with humanintervention; or the like.

The measure used may be the probabilistic matching of certain physicalattributes. This may be associated with automatic record linkage. Typesof matching may include individual matching that may be used for thesingle item scenario, where one file contains a single item and a secondfile contains a the master list of deodorants; many-to-one matching thatmay be a one-to-many matching of individual records from one file to asecond file; single file grouping that may be for grouping similar itemsin a single list; geographic coding that may insert missing informationfrom one file into a second file when there is a match, useful foradding new attribute information from external examples to therepository that does not currently exist; unduplication service that mayidentify duplicate items in a single list; or the like.

Weighting factors of each attribute and of the total composite weight ofthe match may be important aspects to take into account in the matchingprocess. Since the system may use probabilistic matching, the frequencyanalysis of each of the attribute values may be taken into account. Theweight of the match of a specific attribute may be computed as thelogarithm to the base two of the ratio of m and u, where the mprobability is the probability that a field agrees given that the recordpair being examined is a matched pair, which may be one minus the errorrate of the field, and the u probability is the probability that a fieldagrees given that the record pair being examined is an unmatched pair,which may be the probability that the field agrees at random. Thecomposite weight of the match, also referred to as match weight, may bethe sum of each of the matched attribute weights. Each matched attributemay then be computed. If two attributes do not match, the disagreementweight may be calculated as: log₂[(1−m)/(1−u)]. Each time a match isaccomplished, a match weight may also be calculated. Thresholds may beestablished to decide if this is a good match, a partial match, or not amatch at all.

When doing probabilistic matching, different types of attributes may beneeded, such as block attributes and match attributes. In this instance,block attributes may divide a list of items into blocks. This mayprovide a better performance on the matching. The block attributes mayhave to match before the block of items is examined. Two keycats mayhave similar attributes with different sets of attribute values. A valuemay be the information stored in a value fact of an attribute. There maybe different types of attributes, such as global attributes across allkeycats, keycat specific attributes, or the like. A category may also beused as a block. A category may be a classification of items made up ofone to many different full or partial keycats.

Global attributes may be used across a plurality of keycats. Blockattributes for the item domain may come from either the set of global orkeycat specific attributes. Global attributes for the item domain mayinclude keycat, keycat description, system, generation, vendor, item,week added, week completed coding, week last moved, price, UPCdescription, UPC 70 character description, US Item, maintained, brandcode, company code, company name, major brand name, immediate parentcompany, top parent company, brand type, minor brand name, major brandshort description, or the like.

Specific keycat attributes may not be common across keycats. There maybe more descriptive attributes that a consumer would look at. Some ofthese attributes may be common across multiple keycats. Most of thekeycat specific attributes may be used as match attributes. However, foreach keycat, the most important keycat specific attribute may be used asa block attribute. The match attributes may be where the true match forsimilarity occurs. Examples of deodorant keycat attributes may includetotal ounces, total count, base ounces, store location, per unit ounce,product type, package, flavor, scent, strength, additives, form, or thelike. The block and match attributes may be selected from a list ofdeodorant specific attributes, such as per unit ounce, product type,package, flavor, scent, strength, additives, form, or the like.

The similarity process for deciding if items in an item domain aresimilar may use a probabilistic matching engine, such as the IBMWebSphere QualityStage (QS). The process steps may include: extractionof the items from the item dictionary now and the new repository in thefuture, conversion of all volumetric attributes and defined attributesfor the keycat into specific columns in a table using the longdescription as values, formatting the information into a fixed lengthcolumn ASCII file, setting up a new project, entering the data file,mapping the data file columns, setting up and running an investigatestage to develop a frequency analysis of the values for each of theattributes that will be used in the match stage, setting up the blockattributes from the list of deodorant specific attributes, or the like.Another process step may be associated with setting up and running acharacter concatenate investigate stage for each of the attributes, suchas per unit ounce, product type, package, flavor, scent, strength,additives, form, and the like, that may be used in the matching process.

It should be appreciated that the probabilistic matching enginemethodology is but one of the many methods that may be used within thesimilarity facility 180. Others methods may include, but are not limitedto, time series similarity methods, attribute-based methods,spillage-based methods, information theory methods, classificationtrees, or some other similarity methodology, combination of similaritymethodologies, or plurality of methodologies.

In embodiments, referring to FIG. 3, systems and methods may involveusing a platform as disclosed herein for applications described hereinwhere the systems and methods involve identifying a classificationscheme associated with product attributes of a grouping of products ofan entity 6102. It may also involve receiving a record of data relatingto an item of a competitor to the entity, the classification of which isuncertain 6104. It may also involve receiving a dictionary of attributesassociated with products 6108. It may also involve assigning a productcode to the item, based on probabilistic matching among the attributesin the classification scheme, the attributes in the dictionary ofattributes and at least one known attribute of the item 6110.

In embodiments, the product attribute may be a nutritional level, abrand, a product category, a physical attribute, may be based on one ormore part of a stock keeping unit (SKU) or some other type of productattribute. For example, a product attribute may be ‘Reebok’. In thiscase, the product attribute is a brand. The product attributes may beprovided by a similarity facility 180. The similarity facility 180 mayassociate each attribute with a weight for probabilistic matching.

In embodiments, the product attribute may be the physical attribute. Thephysical attribute may be a flavor, a scent, packaging type, a productlaunch type, display location or some other type of physical attribute.For example, the packaging type may be ‘hard bound’ or ‘paperback’ incase of a book.

The elements depicted in flow charts and block diagrams throughout thefigures imply logical boundaries between the elements. However,according to software or hardware engineering practices, the depictedelements and the functions thereof may be implemented as parts of amonolithic software structure, as standalone software modules, or asmodules that employ external routines, code, services, and so forth, orany combination of these, and all such implementations are within thescope of the present disclosure. Thus, while the foregoing drawings anddescription set forth functional aspects of the disclosed systems, noparticular arrangement of software for implementing these functionalaspects should be inferred from these descriptions unless explicitlystated or otherwise clear from the context.

Similarly, it will be appreciated that the various steps identified anddescribed above may be varied, and that the order of steps may beadapted to particular applications of the techniques disclosed herein.All such variations and modifications are intended to fall within thescope of this disclosure. As such, the depiction and/or description ofan order for various steps should not be understood to require aparticular order of execution for those steps, unless required by aparticular application, or explicitly stated or otherwise clear from thecontext.

The methods or processes described above, and steps thereof, may berealized in hardware, software, or any combination of these suitable fora particular application. The hardware may include a general-purposecomputer and/or dedicated computing device. The processes may berealized in one or more microprocessors, microcontrollers, embeddedmicrocontrollers, programmable digital signal processors or otherprogrammable device, along with internal and/or external memory. Theprocesses may also, or instead, be embodied in an application specificintegrated circuit, a programmable gate array, programmable array logic,or any other device or combination of devices that may be configured toprocess electronic signals. It will further be appreciated that one ormore of the processes may be realized as computer executable codecreated using a structured programming language such as C, an objectoriented programming language such as C++, or any other high-level orlow-level programming language (including assembly languages, hardwaredescription languages, and database programming languages andtechnologies) that may be stored, compiled or interpreted to run on oneof the above devices, as well as heterogeneous combinations ofprocessors, processor architectures, or combinations of differenthardware and software.

Thus, in one aspect, each method described above and combinationsthereof may be embodied in computer executable code that, when executingon one or more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in systems that perform the stepsthereof, and may be distributed across devices in a number of ways, orall of the functionality may be integrated into a dedicated, standalonedevice or other hardware. In another aspect, means for performing thesteps associated with the processes described above may include any ofthe hardware and/or software described above. All such permutations andcombinations are intended to fall within the scope of the presentdisclosure.

While the invention has been disclosed in connection with the preferredembodiments shown and described in detail, various modifications andimprovements thereon will become readily apparent to those skilled inthe art. Accordingly, the spirit and scope of the present invention isnot to be limited by the foregoing examples, but is to be understood inthe broadest sense allowable by law.

All documents referenced herein are hereby incorporated by reference.

1. A method comprising: using a computer, identifying a hierarchy ofproducts, each product in the hierarchy having known attributes, whereinthe hierarchy of products includes blocks of products defined by valuesof one or more block attributes of the products in the block; receivinga record of attributes of an unclassified item, the record includingblock attributes and match attributes of the unclassified item; andassigning a block location in the hierarchy to the unclassified item,based on probabilistic matching among the block attributes of theproducts in the hierarchy and at least one known block attribute of theunclassified item, and then assigning a location within the block to theunclassified item, based on probabilistic matching among the matchattributes of the products in the block location and at least one knownmatch attribute of the unclassified item, thereby classifying the item.2. The method of claim 1, further comprising iterating the probabilisticmatching until a statistical criterion is met.
 3. The method of claim 1,wherein the known attribute of the unclassified item is a nutritionallevel.
 4. The method of claim 1, wherein the known attribute of theunclassified item is a brand.
 5. The method of claim 1, wherein theknown attribute of the unclassified item is product category.
 6. Themethod of claim 1, wherein the known attribute of the unclassified itemis based at least in part on a stock keeping unit (SKU).
 7. The methodof claim 1, wherein the known attribute of the unclassified item is aphysical attribute.
 8. The method of claim 7, wherein the physicalattribute is a flavor.
 9. The method of claim 7, wherein the physicalattribute is a scent.
 10. The method of claim 7, wherein the physicalattribute is packaging type.
 11. The method of claim 7, wherein thephysical attribute is a product launch date.
 12. The method of claim 7,wherein the physical attribute is display location.